J-Shuwa: A Large-Scale Web-Collected Japanese Sign Language-Japanese Parallel Corpus

摘要

Japanese Sign Language (JSL) is a low-resource sign language that has received limited attention in the AI research community, primarily due to the lack of large-scale, publicly available parallel corpora. In this work, we introduce J-Shuwa, a large-scale JSL-Japanese parallel corpus constructed from YouTube videos with hard-coded subtitles and closed captions. The corpus contains 197K parallel JSL-Japanese sentence pairs, totaling approximately 300 hours of video, making it the largest publicly available JSL dataset to date. We conduct sign language translation (SLT) experiments by training models on J-Shuwa and evaluating them on the JSL Dialogue Corpus under both zero-shot and fine-tuned settings. Our results demonstrate that J-Shuwa is effective for training SLT models. Beyond SLT, we believe that J-Shuwa can also serve as a valuable resource for future JSL research across a wide range of tasks. The dataset and code are publicly available at: https://github.com/SpaJune/J-Shuwa.